Typically, autonomous robots are capable of concurrent map building of the environment and estimating their relative location, generally termed as simultaneous localization and mapping (SLAM) problem. In an era when commodity hardware is replacing costly, specialized hardware in most scenarios, software reliability within cloud robotic middle-ware may allow its distributed execution on lightweight, low cost robots and network edge devices. However successful functioning of multi-robot systems in critical missions requires resilience in the middle-ware such that the overall functioning degrades gracefully in the face of hardware failure and connectivity failure to the cloud server. Even, multi-robot cooperative SLAM provides reliability, but orienting and merging of maps built by different robot may be both processing and memory intensive task and hence, may not be suitable in a robotic cluster. Also, a failure of a primary robot terminates the map building process leading to failure of the SLAM process.